Artificial Intelligence for Detecting Aortic Arch Calcification on Chest Radiographs: A Systematic Review.

ABSTRACT: Background/Objectives: Aortic-arch calcification (AAC) is a robust predictor of cardiovascular events often overlooked on routine chest radiographs (CXR). This systematic review aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) models for detecting AAC on CXR and assess their potential for clinical implementation. Methods: The review followed PRISMA 2020 guidelines (PROSPERO: CRD420251208627). A search of Embase, PubMed, Scopus, and Web of Science was conducted (Jan 2020-Oct 2025) for studies evaluating AI models detecting AAC in adults. Bias was assessed using QUADAS-2. Due to methodological heterogeneity, a narrative synthesis was performed instead of a meta-analysis. Results: Out of 115 records, three retrospective studies (2022-2024) utilizi

SUBMITTER: Zerdzinski K 

PROVIDER: S-EPMC12839748 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature